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Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation

View ORCID ProfileTomochika Fujisawa, View ORCID ProfileVíctor Noguerales, View ORCID ProfileEmmanouil Meramveliotakis, View ORCID ProfileAnna Papadopoulou, View ORCID ProfileAlfried P. Vogler
doi: https://doi.org/10.1101/2021.12.22.473797
Tomochika Fujisawa
1The Center for Data Science Education and Research, Shiga University, 1-1-1 Banba, Hikone, Shiga 522-8522, Japan
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  • For correspondence: t.fujisawa05@gmail.com victor.noguerales@csic.es
Víctor Noguerales
2Department of Biological Sciences, University of Cyprus, PO Box 20537, Nicosia 1678, Cyprus
3Instituto de Productos Naturales y Agrobiología (IPNA-CSIC), Astrofísico Francisco Sánchez 3, 38206, San Cristóbal de La Laguna, Tenerife, Canary Islands, Spain
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  • For correspondence: t.fujisawa05@gmail.com victor.noguerales@csic.es
Emmanouil Meramveliotakis
2Department of Biological Sciences, University of Cyprus, PO Box 20537, Nicosia 1678, Cyprus
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Anna Papadopoulou
2Department of Biological Sciences, University of Cyprus, PO Box 20537, Nicosia 1678, Cyprus
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Alfried P. Vogler
4Department of Life Sciences, Natural History Museum, Cromwell Rd, London, SW7 5BD, UK
5Department of Life Sciences, Silwood Park Campus, Imperial College London, Ascot, SL5 7PY, UK
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ABSTRACT

Complex bulk samples of invertebrates from biodiversity surveys present a great challenge for taxonomic identification, especially if obtained from unexplored ecosystems. High-throughput imaging combined with machine learning for rapid classification could overcome this bottleneck. Developing such procedures requires that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. Yet the feasibility of transfer learning for the classification of unknown samples remains to be tested. Here, we assess the efficiency of deep learning and domain transfer algorithms for family-level classification of below-ground bulk samples of Coleoptera from understudied forests of Cyprus. We trained neural network models with images from local surveys versus global databases of above-ground samples from tropical forests and evaluated how prediction accuracy was affected by: (a) the quality and resolution of images, (b) the size and complexity of the training set and (c) the transferability of identifications across very disparate source-target pairs that do not share any species or genera. Within-dataset classification accuracy reached 98% and depended on the number and quality of training images and on dataset complexity. The accuracy of between-datasets predictions was reduced to a maximum of 82% and depended greatly on the standardisation of the imaging procedure. When the source and target images were of similar quality and resolution, albeit from different faunas, the reduction of accuracy was minimal. Application of algorithms for domain adaptation significantly improved the prediction performance of models trained by non-standardised, low-quality images. Our findings demonstrate that existing databases can be used to train models and successfully classify images from unexplored biota, when the imaging conditions and classification algorithms are carefully considered. Also, our results provide guidelines for data acquisition and algorithmic development for high-throughput image-based biodiversity surveys.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 23, 2021.
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Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation
Tomochika Fujisawa, Víctor Noguerales, Emmanouil Meramveliotakis, Anna Papadopoulou, Alfried P. Vogler
bioRxiv 2021.12.22.473797; doi: https://doi.org/10.1101/2021.12.22.473797
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Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation
Tomochika Fujisawa, Víctor Noguerales, Emmanouil Meramveliotakis, Anna Papadopoulou, Alfried P. Vogler
bioRxiv 2021.12.22.473797; doi: https://doi.org/10.1101/2021.12.22.473797

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